Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy.

Filariasis, a neglected tropical disease caused by roundworms, is a significant public health concern in many tropical countries. Microscopic examination of blood samples can detect and differentiate parasite species, but it is time consuming and requires expert microscopists, a resource that is not always available. In this context, artificial intelligence (AI) can assist in the diagnosis of this disease by automatically detecting and differentiating microfilariae. In line with the target product profile for lymphatic filariasis as defined by the World Health Organization, we developed an edge AI system running on a smartphone whose camera is aligned with the ocular of an optical microscope that detects and differentiates filarias species in real time without the internet connection. Our object detection algorithm that uses the Single-Shot Detection (SSD) MobileNet V2 detection model was developed with 115 cases, 85 cases with 1903 fields of view and 3342 labels for model training, and 30 cases with 484 fields of view and 873 labels for model validation before clinical validation, is able to detect microfilariae at 10x magnification and distinguishes four species of them at 40x magnification: Loa loa, Mansonella perstans, Wuchereria bancrofti, and Brugia malayi. We validated our augmented microscopy system in the clinical environment by replicating the diagnostic workflow encompassed examinations at 10x and 40x with the assistance of the AI models analyzing 18 samples with the AI running on a middle range smartphone. It achieved an overall precision of 94.14%, recall of 91.90% and F1 score of 93.01% for the screening algorithm and 95.46%, 97.81% and 96.62% for the species differentiation algorithm respectively. This innovative solution has the potential to support filariasis diagnosis and monitoring, particularly in resource-limited settings where access to expert technicians and laboratory equipment is scarce.

Malaria diagnosis using a combined system of a simple and fast extraction method with a lyophilised Dual-LAMP assay in a non-endemic setting.

Malaria is a parasitic disease distributed in tropical areas but with a high number of imported cases in non-endemic countries. The most specific and sensitive malaria diagnostic methods are PCR and LAMP. However, both require specific equipment, extraction procedures and a cold chain. This study aims to solve some limitations of LAMP method with the optimization and validation of six LAMP assays, genus and species-specific, using an easy and fast extraction method, the incorporation of a reaction control assay, two ways (Dual) of result reading and reagent lyophilization. The Dual-LAMP assays were validated against the Nested-Multiplex Malaria PCR. A conventional column and saline extraction methods, and the use of lyophilized reaction tubes were also assessed. A new reaction control Dual-LAMP-RC assay was designed. Dual-LAMP-Pspp assay showed no cross-reactivity with other parasites, repeatability and reproducibility of 100%, a significant correlation between parasite concentration and time to amplification and a LoD of 1.22 parasites/µl and 5.82 parasites/µl using column and saline extraction methods, respectively. Sensitivity and specificity of the six Dual-LAMP assays reach values of 100% or close to this, being lower for the Dual-LAMP-Pm. The Dual-LAMP-RC assay worked as expected. Lyophilized Dual-LAMP results were concordant with the reference method. Dual-LAMP malaria assays with the addition of a new reaction control LAMP assay and the use of a fast and easy saline extraction method, provided low limit of detection, no cross-reactivity, and good sensitivity and specificity. Furthermore, the reagent lyophilization and the dual result reading allow their use in most settings.